Computational evolution of decision-making strategies
This work addresses the challenge of understanding how decision-making strategies develop in response to environments, offering a novel approach for researchers in cognitive science and artificial intelligence, though it is incremental in its application to a specific problem.
The authors tackled the problem of adaptive decision-making by developing a computational evolution method that allows strategies to emerge from environmental pressures, rather than designing them first. They applied this to a dynamic decision-making task and found that more difficult environments led to evolved agents with larger brains and sequential sampling strategies, while easier environments resulted in smaller brains and heuristic strategies.
Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds. We present a method for studying strategy development based on computational evolution that takes the opposite approach, allowing strategies to develop in response to the decision-making environment via Darwinian evolution. We apply this approach to a dynamic decision-making problem where artificial agents make decisions about the source of incoming information. In doing so, we show that the complexity of the brains and strategies of evolved agents are a function of the environment in which they develop. More difficult environments lead to larger brains and more information use, resulting in strategies resembling a sequential sampling approach. Less difficult environments drive evolution toward smaller brains and less information use, resulting in simpler heuristic-like strategies.